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---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.3
- Kukedlc/NeuralSynthesis-7B-v0.1
- mlabonne/AlphaMonarch-7B
- s3nh/SeverusWestLake-7B-DPO
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
- Kukedlc/NeuralSynthesis-7B-v0.1
- mlabonne/AlphaMonarch-7B
- s3nh/SeverusWestLake-7B-DPO
---
# MixtureofMerges-MoE-4x7b-v10-MIXTRAL3
MixtureofMerges-MoE-4x7b-v10-MIXTRAL3 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
* [Kukedlc/NeuralSynthesis-7B-v0.1](https://huggingface.co/Kukedlc/NeuralSynthesis-7B-v0.1)
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [s3nh/SeverusWestLake-7B-DPO](https://huggingface.co/s3nh/SeverusWestLake-7B-DPO)
## 🧩 Configuration
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.3
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: mistralai/Mistral-7B-Instruct-v0.3
positive_prompts:
- "Analyze the ARC (Argument Reasoning Comprehension) question."
- "Use logical reasoning and common sense."
- "Identify the assumptions in this argument."
- "Evaluate the validity of these assumptions."
- "Provide an alternative explanation for this argument."
- "Identify weaknesses in this argument."
- "Detect any logical fallacies in this argument and specify them."
negative_prompts:
- "ignores key evidence"
- "too general"
- "focuses on irrelevant details"
- "assumes unprovided information"
- "relies on stereotypes"
- source_model: Kukedlc/NeuralSynthesis-7B-v0.1
positive_prompts:
- "Answer with commonsense understanding and relevant general knowledge."
- "Summarize this passage and explain the importance of the highlighted section."
- "Compare two articles with different viewpoints and list their key arguments."
- "Paraphrase this statement, altering the emotional tone but retaining the core meaning."
- "Create an analogy to illustrate the main concept of this article."
negative_prompts:
- "overly simplistic"
- "understates important points"
- "ignores critical details"
- "misses the question's nuance"
- "takes the statement too literally"
- source_model: mlabonne/AlphaMonarch-7B
positive_prompts:
- "Solve this math problem."
- "Demonstrate strong mathematical capabilities."
- "Solve for the given variable."
- "Calculate the total cost for 12 apples at $0.50 each."
- "Isolate the variable in the equation: 2x + 5 = 17."
- "Show your work in solving this equation."
- "Explain the formula used to solve the problem."
- "Discuss why dividing by zero is impossible."
negative_prompts:
- "incorrect calculation"
- "inaccurate answer"
- "lacks creativity"
- "assumes without proof"
- "rushed calculation"
- "confuses concepts"
- "draws illogical conclusions"
- "circular reasoning"
- source_model: s3nh/SeverusWestLake-7B-DPO
positive_prompts:
- "Generate possible continuations for this scenario."
- "Show understanding of everyday commonsense."
- "Use contextual clues to predict the outcome."
- "Continue the scenario in a cool and informal style."
- "Introduce an unexpected yet plausible twist to the narrative."
- "Depict a character's angry outburst in this scenario."
negative_prompts:
- "repetitive phrases"
- "overuse of words"
- "contradicts previous statements"
- "unnatural dialogue"
- "awkward phrasing"
- "mismatched genre"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jsfs11/MixtureofMerges-MoE-4x7b-v10-MIXTRAL3"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |